CN113851185A - 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 - Google Patents
一种用于非小细胞肺癌患者免疫治疗的预后评估方法 Download PDFInfo
- Publication number
- CN113851185A CN113851185A CN202111433361.1A CN202111433361A CN113851185A CN 113851185 A CN113851185 A CN 113851185A CN 202111433361 A CN202111433361 A CN 202111433361A CN 113851185 A CN113851185 A CN 113851185A
- Authority
- CN
- China
- Prior art keywords
- model
- patient
- prognosis
- tmb
- lung cancer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004393 prognosis Methods 0.000 title claims abstract description 27
- 208000002154 non-small cell lung carcinoma Diseases 0.000 title claims abstract description 20
- 208000029729 tumor suppressor gene on chromosome 11 Diseases 0.000 title claims abstract description 20
- 238000009169 immunotherapy Methods 0.000 title claims abstract description 18
- 238000011156 evaluation Methods 0.000 title claims abstract description 9
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 33
- 238000010837 poor prognosis Methods 0.000 claims abstract 2
- 238000000034 method Methods 0.000 claims description 15
- 238000003066 decision tree Methods 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 8
- 238000013138 pruning Methods 0.000 claims description 7
- 238000012795 verification Methods 0.000 claims description 6
- 206010064571 Gene mutation Diseases 0.000 claims description 5
- 238000002790 cross-validation Methods 0.000 claims description 5
- 238000012350 deep sequencing Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 4
- 230000035772 mutation Effects 0.000 claims description 4
- 102100022983 B-cell lymphoma/leukemia 11B Human genes 0.000 claims description 3
- 102100038595 Estrogen receptor Human genes 0.000 claims description 3
- 102100034553 Fanconi anemia group J protein Human genes 0.000 claims description 3
- 101001095815 Homo sapiens E3 ubiquitin-protein ligase RING2 Proteins 0.000 claims description 3
- 101000882584 Homo sapiens Estrogen receptor Proteins 0.000 claims description 3
- 101000848171 Homo sapiens Fanconi anemia group J protein Proteins 0.000 claims description 3
- 101000574654 Homo sapiens GTP-binding protein Rit1 Proteins 0.000 claims description 3
- 101001057193 Homo sapiens Membrane-associated guanylate kinase, WW and PDZ domain-containing protein 1 Proteins 0.000 claims description 3
- 101000616523 Homo sapiens SH2B adapter protein 3 Proteins 0.000 claims description 3
- 101000874160 Homo sapiens Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial Proteins 0.000 claims description 3
- 101000795659 Homo sapiens Tuberin Proteins 0.000 claims description 3
- 101000740048 Homo sapiens Ubiquitin carboxyl-terminal hydrolase BAP1 Proteins 0.000 claims description 3
- 101000740049 Latilactobacillus curvatus Bioactive peptide 1 Proteins 0.000 claims description 3
- 102100027240 Membrane-associated guanylate kinase, WW and PDZ domain-containing protein 1 Human genes 0.000 claims description 3
- 101100334745 Mus musculus Fgfr4 gene Proteins 0.000 claims description 3
- 102100021778 SH2B adapter protein 3 Human genes 0.000 claims description 3
- 101150045565 Socs1 gene Proteins 0.000 claims description 3
- 102100035726 Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial Human genes 0.000 claims description 3
- 108700027336 Suppressor of Cytokine Signaling 1 Proteins 0.000 claims description 3
- 102100024779 Suppressor of cytokine signaling 1 Human genes 0.000 claims description 3
- 102100031638 Tuberin Human genes 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000002688 persistence Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 102000047459 trkC Receptor Human genes 0.000 claims description 3
- 108010064892 trkC Receptor Proteins 0.000 claims description 3
- 101000599886 Homo sapiens Isocitrate dehydrogenase [NADP], mitochondrial Proteins 0.000 claims description 2
- 102100037845 Isocitrate dehydrogenase [NADP], mitochondrial Human genes 0.000 claims description 2
- -1 MET Proteins 0.000 claims description 2
- 108700028341 SMARCB1 Proteins 0.000 claims description 2
- 101150008214 SMARCB1 gene Proteins 0.000 claims description 2
- 102100025746 SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 Human genes 0.000 claims description 2
- 238000013145 classification model Methods 0.000 claims description 2
- 230000007614 genetic variation Effects 0.000 claims description 2
- 238000010801 machine learning Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 claims description 2
- 230000008901 benefit Effects 0.000 abstract description 6
- 230000004083 survival effect Effects 0.000 description 5
- 239000000523 sample Substances 0.000 description 4
- 101150025032 13 gene Proteins 0.000 description 3
- 206010028980 Neoplasm Diseases 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 239000003814 drug Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 206010067484 Adverse reaction Diseases 0.000 description 1
- 108010074708 B7-H1 Antigen Proteins 0.000 description 1
- 102000008096 B7-H1 Antigen Human genes 0.000 description 1
- 101000599885 Dictyostelium discoideum Isocitrate dehydrogenase [NADP], mitochondrial Proteins 0.000 description 1
- 229940076838 Immune checkpoint inhibitor Drugs 0.000 description 1
- 108091008026 Inhibitory immune checkpoint proteins Proteins 0.000 description 1
- 102000037984 Inhibitory immune checkpoint proteins Human genes 0.000 description 1
- 208000037844 advanced solid tumor Diseases 0.000 description 1
- 230000006838 adverse reaction Effects 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000017188 evasion or tolerance of host immune response Effects 0.000 description 1
- 210000000987 immune system Anatomy 0.000 description 1
- 239000012274 immune-checkpoint protein inhibitor Substances 0.000 description 1
- 239000003112 inhibitor Substances 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 239000013610 patient sample Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 210000004881 tumor cell Anatomy 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
- C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
- C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
- C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/50—Mutagenesis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/118—Prognosis of disease development
-
- C—CHEMISTRY; METALLURGY
- C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- C12Q2600/00—Oligonucleotides characterized by their use
- C12Q2600/156—Polymorphic or mutational markers
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Chemical & Material Sciences (AREA)
- Public Health (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Pathology (AREA)
- Biotechnology (AREA)
- Biophysics (AREA)
- Databases & Information Systems (AREA)
- Genetics & Genomics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Epidemiology (AREA)
- Analytical Chemistry (AREA)
- Organic Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Biology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Molecular Biology (AREA)
- Primary Health Care (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Biomedical Technology (AREA)
- Immunology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Bioethics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Oncology (AREA)
- Microbiology (AREA)
- Hospice & Palliative Care (AREA)
- Biochemistry (AREA)
- Physiology (AREA)
Abstract
本发明涉及医学分子生物学技术领域,特别涉及一种用于非小细胞肺癌患者免疫治疗的预后评估方法,包括通过测序检测基因变异、创建决策树模型并筛选出13个最优特征基因、构建预测模型对非小细胞肺癌TMB‑H患者进行预后风险评分,从而预测出病人的预后情况。与现有技术相比,本发明提供的一种用于非小细胞肺癌TMB‑H患者免疫治疗的预后评估方法,通过对非小细胞肺癌TMB‑H患者样本进行测序,基于CART算法,构建包含13个最优的特征基因的预测模型,根据预测模型对非小细胞肺癌TMB‑H患者进行预后风险评分,将TMB‑H人群分为预后良好组与预后较差组,准确率高达0.85。
Description
技术领域
本发明涉及医学分子生物学技术领域,特别涉及一种用于非小细胞肺癌患者免疫治疗的预后评估方法。
背景技术
免疫治疗药物通过抑制肿瘤细胞的免疫逃逸,调动患者自身免疫***功能消除肿瘤。目前免疫治疗已在多种晚期实体肿瘤治疗中取得了突破性进展,尤其是可有效延长患者总生存期(Overall survival,OS),且不良反应可控。但由于缺乏合适的临床分子标志物,PD-1/PD-L1免疫治疗药物的受益人群只有20%-30%。TMB的精确测量可以预测免疫检查点抑制剂的疗效,使癌症患者有机会获得更加精准的治疗。既往临床研究及转化研究显示,基于组织检测肿瘤突变负荷(Tumor Mutation Burden,TMB)状态与客观缓解率(Objective response rate,ORR)、无进展生存期(Progression-free survival,PFS)以及OS相关,因此被认为是指导免疫治疗的重要标记物。一般认为TMB值较高时(TMB-H),免疫检验点抑制剂效果较好。但有大量报道指出当使用TMB作为指标进行免疫治疗获益人群划分时,存在特异度较差情况,具体表现为TMB-H非小细胞肺癌人群中存在免疫治疗后OS较短患者。亟待其他标志物辅助TMB进行人群的二次划分,提高TMB-H免疫治疗获益人群分类效果。
发明内容
针对以上述背景技术的不足,本发明提供一种用于非小细胞肺癌患者免疫治疗的预后评估方法。
本发明采用的技术方案如下:一种用于非小细胞肺癌患者免疫治疗的预后评估方法,关键在于:包括以下步骤:
S1. 对非小细胞肺癌患者进行基因靶向测序,获取基因变异情况;
S2. 将患者基因突变列表与预后情况输入监督学习决策树模型中,基于CART算法,建立分类模型,创建决策树模型,筛选出13个最优特征基因;
S3. 构建包含13个最优的特征基因的预测模型式I,用于对非小细胞肺癌TMB-H患者进行预后风险评分,从而预测出病人的预后情况;
优选的,所述S1具体为:采集非小细胞肺癌TMB-H患者样本进行靶向深度测序,检测panel基因变异情况。
优选的,所述S2具体为:将检测到的基因变异做为训练特征,以总生存期OS<12 月以及总生存期OS>12月作为机器学习的分类结果,使用监督学习决策树模型CART算法,通过特征选择、剪枝、交叉验证、模型持久化四个步骤,创建决策树模型。
优选的,所述特征选择具体为:使用公式II所示的基尼系数作为衡量标准,来计算通过不同的特征进行分支选择后的分类情况,找出最好的分类特征作为根节点,并以此类推,直到建树结束,创建复杂树模型;
其中K表示类别, p为第k个类别的概率,Gini(p)越小,则纯度越高,特征越好。
优选的,所述剪枝具体为:通过公式III所示的极小化决策树整体的损失函数来实现,对所述复杂树模型自下往上的对非叶子结点进行考回缩,若将该结点对应的子树替换为叶结点能带来泛化性能提升,则进行剪枝,即将父结点变为新的叶子结点,从而对将生成的树进行简化,以避免过拟合情况的发生;
其中,C(T)表示模型与训练数据的拟合程度,|T|表示模型复杂度。
优选的,所述交叉验证具体为:通过公式IV所示的准确度公式,将原始数据集随机地分成5份,每次将其中4份作为训练集来训练模型,剩余1份作为验证集验证模型,得到验证集的准确度,轮流进行5次,直到所有的数据都被验证了一次且仅被验证一次,循环计算每组模型准确度得分平均值,取平均值最高为最优模型,从而评价模型的泛化能力,从而进行模型及参数选择;
其中,Accuracy 代表准确率,TP:被正确分类的正例样本个数,TN:被正确分类的负例样本个数,FP:被错误分类的负例样本个数,FN:被错误分类的正例样本个数。
优选的,所述13个最优特征基因为SMARCB1、TSC2、 BAP1、SDHB、RIT1、ESR1、SOCS1、SH2B3、IDH2、MET、 BRIP1、NTRK3、FGFR4。
优选的,将TMB-H患者的样本进行靶向深度测序,检测基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后较好组与较差组。
有益效果:与现有技术相比,本发明提供的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,通过采集非小细胞肺癌TMB-H患者样本,基于CART算法,构建包含13个最优的特征基因的预测模型,根据预测模型对非小细胞肺癌TMB-H患者进行预后风险评分,将TMB-H人群分为预后较好组与较差组,准确率高达0.85。
附图说明
图1为本发明预测模型的单因素cox回归分析图;
图2为13个基因突变与整体生存相关性示意图;
图3为不同基因构建模型对TMB-H人群的DCR示意图。
具体实施方式
为使本领域技术人员更好的理解本发明的技术方案,下面结合附图和具体实施方式对本发明作详细说明。
实施例1
随机选择TMB-H组的155个患者,对各患者样本进行基因靶向测序,将各患者基因突变列表与预后情况输入监督学习决策树模型中,基于CART算法,使用Python (3.7.0)sklearn.tree模块DecisionTreeClassifier函数进行特征选择和剪枝;使用sklearn.model_selection模块cross_val_score函数进行五折交叉验证,并计算模型准确度;使用joblib模块进行模型持久化;使用graphviz模块绘制决策树模型,最终决策树模型包含13个最优的特征基因,13个最优特征基因包括9个负向预测基因(SMARCB1,TSC2,BAP1,SDHB,RIT1,ESR1, SOCS1,SH2B3,IDH2)和4个正向预测基因(MET,BRIP1, NTRK3,FGFR4);根据筛选的13个最优的特征基因,构建包含13个最优的特征基因的预测模型(式1),(式1),进行单因素cox回归分析(见图1)。结果显示,13个基因突变与整体生存存在显著相关性(p<0.05);
将TMB-H患者的样本进行靶向深度测序,检测panel基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后高风险和低风险组(见图2),结果显示接受免疫治疗后,TMB-H低风险组预后生存效果显著优于TMB-H高风险组与TMB-L组。
对比例
将9个负向基因、4个正向基因与13个综合基因分别构建模型对TMB-H人群进行划分,并比较人群接受免疫治疗后获益情况(DCR),如图3所示,结果表明,负向9基因、正向4基因与综合13基因划分的不同风险人群预后存在显著差异,并且综合13基因分类人群临床获益差异最为明显。
最后需要说明,上述描述仅为本发明的优选实施例,本领域的技术人员在本发明的启示下,在不违背本发明宗旨及权利要求的前提下,可以做出多种类似的表示,这样的变换均落入本发明的保护范围之内。
Claims (8)
2.根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于所述S1具体为:采集非小细胞肺癌TMB-H患者样本进行靶向深度测序,检测基因变异情况。
3. 根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于所述S2具体为:将检测到的基因变异做为训练特征,以总生存期OS<12 月以及总生存期OS>12月作为机器学习的分类结果,使用监督学习决策树模型CART算法,通过特征选择、剪枝、交叉验证、模型持久化四个步骤,创建决策树模型。
7. 根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于:所述13个最优特征基因为SMARCB1、TSC2、 BAP1、SDHB、RIT1、ESR1、SOCS1、SH2B3、IDH2、MET、 BRIP1、NTRK3、FGFR4。
8.根据权利要求1所述的一种用于非小细胞肺癌患者免疫治疗的预后评估方法,其特征在于:将TMB-H患者的样本进行靶向深度测序,检测panel基因变异,将其13个特征基因的突变情况带入预测模型中计算TMB-H患者预后风险得分,以中位数为区分阈值,将患者分为预后良好组与预后较差组。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111433361.1A CN113851185B (zh) | 2021-11-29 | 2021-11-29 | 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111433361.1A CN113851185B (zh) | 2021-11-29 | 2021-11-29 | 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113851185A true CN113851185A (zh) | 2021-12-28 |
CN113851185B CN113851185B (zh) | 2022-04-19 |
Family
ID=78982240
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111433361.1A Active CN113851185B (zh) | 2021-11-29 | 2021-11-29 | 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113851185B (zh) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114282624A (zh) * | 2021-12-29 | 2022-04-05 | 北京天融信网络安全技术有限公司 | 对象标注方法、装置、设备及介质 |
CN114999653A (zh) * | 2022-06-17 | 2022-09-02 | 中国医学科学院肿瘤医院 | 一种非小细胞肺癌免疫治疗疗效的预测模型的训练方法以及预测装置 |
CN115620894A (zh) * | 2022-09-20 | 2023-01-17 | 贵州医科大学第二附属医院 | 基于基因突变的肺癌免疫疗效预测方法、***及存储介质 |
CN115938579A (zh) * | 2022-11-30 | 2023-04-07 | 常州国药医学检验实验室有限公司 | 一种预测非小细胞肺癌患者生存率的特征组合及Cox比例风险模型 |
CN116844685A (zh) * | 2023-07-03 | 2023-10-03 | 广州默锐医药科技有限公司 | 一种免疫治疗效果评估方法、装置、电子设备及存储介质 |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090062144A1 (en) * | 2007-04-03 | 2009-03-05 | Nancy Lan Guo | Gene signature for prognosis and diagnosis of lung cancer |
US20100272635A1 (en) * | 2007-06-15 | 2010-10-28 | Rodems Kelline M | Methods and compositions for diagnosis and/or prognosis in ovarian cancer and lung cancer |
US20110256545A1 (en) * | 2010-04-14 | 2011-10-20 | Nancy Lan Guo | mRNA expression-based prognostic gene signature for non-small cell lung cancer |
WO2014163445A1 (ko) * | 2013-04-05 | 2014-10-09 | 연세대학교 산학협력단 | 위암에 대한 예후 예측 모형의 제조방법 |
WO2017074036A2 (ko) * | 2015-10-26 | 2017-05-04 | 주식회사 싸이퍼롬 | 암 환자의 유전체 염기서열 변이 정보와 생존 정보를 이용한 맞춤형 약물 선택 방법 및 시스템 |
-
2021
- 2021-11-29 CN CN202111433361.1A patent/CN113851185B/zh active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090062144A1 (en) * | 2007-04-03 | 2009-03-05 | Nancy Lan Guo | Gene signature for prognosis and diagnosis of lung cancer |
US20100272635A1 (en) * | 2007-06-15 | 2010-10-28 | Rodems Kelline M | Methods and compositions for diagnosis and/or prognosis in ovarian cancer and lung cancer |
US20110256545A1 (en) * | 2010-04-14 | 2011-10-20 | Nancy Lan Guo | mRNA expression-based prognostic gene signature for non-small cell lung cancer |
WO2014163445A1 (ko) * | 2013-04-05 | 2014-10-09 | 연세대학교 산학협력단 | 위암에 대한 예후 예측 모형의 제조방법 |
WO2017074036A2 (ko) * | 2015-10-26 | 2017-05-04 | 주식회사 싸이퍼롬 | 암 환자의 유전체 염기서열 변이 정보와 생존 정보를 이용한 맞춤형 약물 선택 방법 및 시스템 |
Non-Patent Citations (3)
Title |
---|
杜佳等: "556例非手术男性肺癌预后分析", 《检验医学与临床》 * |
祁亮等: "TCGA数据库基因突变信息结合机器学习软件RapidMiner构建肝细胞癌患者复发模型", 《中国肝脏病杂志(电子版)》 * |
薛洋等: "EGFR和KRAS突变对手术切除NSCLC患者预后预测价值分析", 《中华肿瘤防治杂志》 * |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114282624A (zh) * | 2021-12-29 | 2022-04-05 | 北京天融信网络安全技术有限公司 | 对象标注方法、装置、设备及介质 |
CN114999653A (zh) * | 2022-06-17 | 2022-09-02 | 中国医学科学院肿瘤医院 | 一种非小细胞肺癌免疫治疗疗效的预测模型的训练方法以及预测装置 |
CN114999653B (zh) * | 2022-06-17 | 2023-06-20 | 中国医学科学院肿瘤医院 | 一种非小细胞肺癌免疫治疗疗效的预测模型的训练方法以及预测装置 |
CN115620894A (zh) * | 2022-09-20 | 2023-01-17 | 贵州医科大学第二附属医院 | 基于基因突变的肺癌免疫疗效预测方法、***及存储介质 |
CN115620894B (zh) * | 2022-09-20 | 2023-05-02 | 贵州医科大学第二附属医院 | 基于基因突变的肺癌免疫疗效预测***、装置及存储介质 |
CN115938579A (zh) * | 2022-11-30 | 2023-04-07 | 常州国药医学检验实验室有限公司 | 一种预测非小细胞肺癌患者生存率的特征组合及Cox比例风险模型 |
CN116844685A (zh) * | 2023-07-03 | 2023-10-03 | 广州默锐医药科技有限公司 | 一种免疫治疗效果评估方法、装置、电子设备及存储介质 |
CN116844685B (zh) * | 2023-07-03 | 2024-04-12 | 广州默锐医药科技有限公司 | 一种免疫治疗效果评估方法、装置、电子设备及存储介质 |
Also Published As
Publication number | Publication date |
---|---|
CN113851185B (zh) | 2022-04-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN113851185B (zh) | 一种用于非小细胞肺癌患者免疫治疗的预后评估方法 | |
CN112888459B (zh) | 卷积神经网络***及数据分类方法 | |
US20190316209A1 (en) | Multi-Assay Prediction Model for Cancer Detection | |
CN109072309B (zh) | 癌症进化检测和诊断 | |
CN112735513B (zh) | 基于dna甲基化谱的肿瘤免疫检查点抑制剂治疗有效性评估模型的构建方法 | |
US20210358626A1 (en) | Systems and methods for cancer condition determination using autoencoders | |
US20210102262A1 (en) | Systems and methods for diagnosing a disease condition using on-target and off-target sequencing data | |
WO2023197825A1 (zh) | 多癌种早筛模型构建方法以及检测装置 | |
CN111748633A (zh) | 一种特征miRNA表达谱组合及头颈鳞状细胞癌早期预测方法 | |
CN113823356B (zh) | 一种甲基化位点识别方法及装置 | |
CN111944902A (zh) | 一种基于lincRNA表达谱组合特征的肾***状细胞癌早期预测方法 | |
CN106415563A (zh) | 用于预测个体的吸烟状况的***和方法 | |
CN111733251A (zh) | 一种特征miRNA表达谱组合及肾透明细胞癌早期预测方法 | |
CN110373458A (zh) | 一种地中海贫血检测的试剂盒及分析*** | |
US20240194294A1 (en) | Artificial-intelligence-based method for detecting tumor-derived mutation of cell-free dna, and method for early diagnosis of cancer, using same | |
US20190108311A1 (en) | Site-specific noise model for targeted sequencing | |
KR20230064172A (ko) | 세포유리 핵산단편 위치별 서열 빈도 및 크기를 이용한 암 진단 방법 | |
CN114496097A (zh) | 一种胃癌代谢基因预后预测方法和装置 | |
CN111733252A (zh) | 一种特征miRNA表达谱组合及胃癌早期预测方法 | |
Madjar | Survival models with selection of genomic covariates in heterogeneous cancer studies | |
CN111808965A (zh) | 一种特征lincRNA表达谱组合及肾透明细胞癌早期预测方法 | |
Wang et al. | Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion | |
CN111944901A (zh) | 一种特征mRNA表达谱组合及肾***状细胞癌早期预测方法 | |
Kariotis | Unsupervised machine learning of high dimensional data for patient stratification | |
CN111793691A (zh) | 一种特征mRNA表达谱组合及肺鳞癌早期预测方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CP03 | Change of name, title or address |
Address after: Room 135, Floor 1, Building 3, No. 96, Longchuanwu Road, Donghu Street, Linping District, Hangzhou City, Zhejiang Province, 310000 Patentee after: Qiuzhen Medical Technology (Zhejiang) Co.,Ltd. Address before: 100000 101 / F, building 3, No. 156 Jinghai 4th Road, Beijing Economic and Technological Development Zone, Daxing District, Beijing Patentee before: CHOSENMED TECHNOLOGY (BEIJING) Co.,Ltd. |
|
CP03 | Change of name, title or address |