WO2023123005A1 - 评估肿瘤免疫微环境的评分模型及其构建方法 - Google Patents

评估肿瘤免疫微环境的评分模型及其构建方法 Download PDF

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WO2023123005A1
WO2023123005A1 PCT/CN2021/142257 CN2021142257W WO2023123005A1 WO 2023123005 A1 WO2023123005 A1 WO 2023123005A1 CN 2021142257 W CN2021142257 W CN 2021142257W WO 2023123005 A1 WO2023123005 A1 WO 2023123005A1
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immune
tumor
data set
sample
component
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French (fr)
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张诗影
王春丽
蔡宇航
石太平
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天津华大医学检验所有限公司
深圳华大基因股份有限公司
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Priority to PCT/CN2021/142257 priority Critical patent/WO2023123005A1/zh
Priority to CN202180102574.8A priority patent/CN118056133A/zh
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer

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  • the present disclosure relates to the field of bioinformatics, and in particular, the present disclosure relates to a scoring model for evaluating tumor immune microenvironment and its construction method.
  • the tumor microenvironment (Tumor microvironment, TME) is a highly complex and dynamic system, mainly composed of tumor cells and their surrounding immune cells, tumor-associated fibroblasts, nearby interstitial tissues, microvessels, etc.
  • TME tumor microvironment
  • tumor cells are in frequent communication with their neighboring cells. This communication is dynamic and two-way. In the communication, the tumor constantly changes the conditions for maintaining its own survival and development, which in turn affects its growth, invasion and metastasis. .
  • the theory of tumor immunoediting as the currently recognized theory of tumor immune escape, divides the development of tumors into three stages, of which the first stage - the clearance period, the immune surveillance function of the body exerts an anti-tumor effect through the anti-tumor immune effector mechanism.
  • the reason why the immune system plays an important role in immune surveillance is that immune cells of the adaptive immune system (such as T cells, B cells) and immune cells of the innate immune system (such as NK cells, DC cells, etc.) infiltrate into the tumor
  • immune cells of the adaptive immune system such as T cells, B cells
  • immune cells of the innate immune system such as NK cells, DC cells, etc.
  • the tumor In the third stage of tumor development—the immune escape stage, the tumor has the function of resisting the elimination of the immune system through the immunoediting of the second stage of balance, and develops into a tumor with clinical manifestations.
  • the present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.
  • the present disclosure provides a method for constructing a scoring model for assessing the tumor immune microenvironment. Using this method to construct the obtained scoring model, the extent of the immune components of the tumor microenvironment is comprehensively measured.
  • the model focuses on various The difference between the immune cell components and the average level converts the difference level that is difficult to superimpose into a probability model, and solves the problem of non-uniform dimensions of the infiltration level.
  • the present disclosure proposes a method for constructing a scoring model for evaluating tumor immune microenvironment.
  • the method comprises:
  • step (3) using the infiltration score of each immune cell type component obtained in step (2) to determine the distribution interval of the corresponding immune cell type component score, and obtain the probability distribution of tumor infiltrating immune cell components;
  • the scoring model is:
  • the p cell_i represents the probability of occurrence of a type of cell component score belonging to the immune component in the sample
  • i represents the cell type
  • the ICS is the response of a type of cell component belonging to the immune cell component in the sample to the immune system. component contribution.
  • RNAseq methods for assessing tumor-infiltrating cellular components cannot fully reflect indicators of immune infiltration levels.
  • the current popular Estimate algorithm was originally developed to estimate the purity of the tumor, and it was not designed to reflect the tumor immune microenvironment. Based on this, the inventor obtained the method for constructing a scoring model for assessing the tumor immune microenvironment described in the first aspect above, and used this method to construct the obtained scoring model, which is different from the previous tumor immune microenvironment assessment model.
  • the degree of contribution quantifies the degree to which various types of cells deviate from the general level, reflecting the difference between the overall level and the general level of various types of immune cells in the microenvironment, and can comprehensively evaluate the immune microenvironment, so as to realize that the difference in the immune microenvironment is important for prognosis. impact research.
  • the data further includes a supplementary data set
  • the supplementary data set includes transcriptome sequencing data of multiple cancer samples and standard samples.
  • the standard sample is selected from a human universal reference RNA standard sample.
  • step (2) further includes:
  • step (3) further includes:
  • the distribution interval is equally divided into 8-12 sub-intervals.
  • the infiltration score of each immune cell type component is obtained by a tumor immune infiltration component analysis software.
  • the tumor immune infiltration component analysis software is selected from Timer software, CIBERSORT software or xCell software.
  • the tumor immune infiltration component analysis software is xCell software.
  • the method for constructing a scoring model for assessing the tumor immune microenvironment integrates the levels of various cell components in tumors calculated based on algorithms such as xCell to comprehensively measure the tumor immune microenvironment .
  • xCell examines more cell types, up to 64 types, including 34 types of immune cells, which is unmatched by other software.
  • the second aspect of the present disclosure provides a scoring model for assessing tumor immune microenvironment.
  • the scoring model is constructed by the construction method described in the first aspect.
  • the third aspect of the present disclosure provides the use of the scoring model for evaluating the tumor immune microenvironment described in the second aspect in evaluating the tumor immune microenvironment.
  • the fourth aspect of the present disclosure provides a method for assessing tumor immune microenvironment. According to an embodiment of the present disclosure, the method comprises:
  • the scoring model for assessing the tumor immune microenvironment described in the second aspect Utilize the scoring model for assessing the tumor immune microenvironment described in the second aspect to obtain the ICS value of the cells belonging to the immune component of the predetermined cancer type in the sample to be tested, compare the ICS value with the preset ICS value, and evaluate the tumor immune microenvironment.
  • the fifth aspect of the present disclosure provides the use of the scoring model for assessing the tumor immune microenvironment described in the second aspect in the prognosis assessment of tumor patients.
  • the sixth aspect of the present disclosure provides a method for evaluating the prognosis of tumor patients. According to an embodiment of the present disclosure, the method comprises:
  • ICS indicators were significantly correlated with patient survival, even if these patients did not receive immunotherapy. Therefore, according to the relationship between the ICS index and the survival analysis of tumor patients, the prognosis of tumor patients can be obtained.
  • step I) further includes:
  • the overall survival or progression-free survival is obtained, and the overall survival or progression-free survival is used as the survival data to obtain the relationship between the ICS value and the survival analysis of tumor patients .
  • the clinical follow-up data includes the patient's survival time, survival status, and response to immune checkpoint inhibitor treatment.
  • the seventh aspect of the present disclosure provides the use of the scoring model for evaluating the tumor immune microenvironment described in the second aspect in evaluating the efficacy of immunotherapy.
  • the eighth aspect of the present disclosure provides a method for evaluating the curative effect of immunotherapy. According to an embodiment of the present disclosure, the method comprises:
  • the inventors found that, according to the patient's immunotherapy data set, the grouping relationship of ICS value combined with the expression of tumor gene markers was found to be related to the efficacy of immunotherapy. Therefore, according to the expression levels of ICS indicators and gene markers in tumor patients before immunotherapy, the efficacy of immunotherapy can be evaluated.
  • Figure 1 shows a flow chart of a method for constructing a scoring model for assessing tumor immune microenvironment according to an embodiment of the present disclosure
  • Fig. 2A-2V has shown the TCGA data set survival analysis result figure that PFI is significantly correlated among the embodiment 1;
  • Figure 3 shows the relationship between ICS and prognosis in Example 2.
  • Figure 4 shows the relationship between ICS in Example 2 and the curative effect of immunotherapy, wherein the degree of immunotherapy response is represented by different colors, black represents complete response, dark gray represents partial response, and light gray represents disease progression.
  • the size of the dot represents the survival time of the sample, and the larger the dot, the longer the survival time of the patient.
  • the living status is represented by different shapes, the circle represents the patient is still alive, and the triangle represents the patient has died. Two dotted lines are added in the figure as an aid to divide the sample into four parts.
  • first and second are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features.
  • the features defined as “first” and “second” may explicitly or implicitly include at least one of these features.
  • “plurality” means at least two, such as two, three, etc., unless otherwise specifically defined.
  • the inventors developed an ICS (Immune Contribution Signature) scoring model.
  • a method for constructing a scoring model for evaluating the tumor immune microenvironment includes steps:
  • the data includes the analysis data set, the reference data set by cancer type, and optionally, the supplementary data set.
  • Levels of immune components were quantified based on the scores obtained for the individual immune cell type components. It is found that different types of cell components have different score distribution ranges. Some cell components have higher overall scores, while others have lower scores. Dimensionality is a critical issue for comprehensive estimation of tumor-infiltrating immune components. If the scores of all immune cell components are simply summed, the proportion of cell types with a high level of infiltration will be large, while the proportion of cell types with a low level of infiltration will be small or even negligible Excluding. In order to eliminate the bias caused by the different dimensions, the evaluation of the infiltration level of an immune cell was transformed into the evaluation of the contribution of the cell to the immune component.
  • the inventor believes that if the infiltration level of a certain immune cell in a sample is significantly different from that of a large number of other samples, such as significantly higher than that of other samples, then the contribution of this type of cells to the immune components in this sample will be greater than that of other samples. The contribution of this cell in the sample. Therefore, the infiltration level distribution can be calculated according to the infiltration level of each cell type in a large number of samples, and then the probability of each type of cell in each sample to be investigated appears in the distribution. If the probability of occurrence is low, the contribution is considered high. , if the probability of occurrence is high, its contribution is considered low.
  • the immune component contribution of the sample is calculated according to the following formula:
  • p cell_i represents the probability of a certain type of cell component belonging to the immune component in the sample
  • i represents the cell type
  • ICS is the contribution of a certain type of cell component belonging to the immune component in the sample to the immune component.
  • Kaplan Meier survival analysis was performed on the samples with calculated ICS using the R software packages survival and survminer.
  • the ICS score is a continuous variable without a clear threshold division.
  • the surv_cutpoint function is used to determine the optimal grouping threshold for survival analysis, and the ICS-high group and ICS-low group are divided by this threshold, and then the survival analysis is performed.
  • the p value of survival analysis was less than 0.05, which indicated that ICS was significantly related to prognosis.
  • the CD274 gene expression value of each sample in the extracted and analyzed data set is used as the x-axis coordinate
  • the ICS value is used as the y-axis coordinate.
  • the degree of immunotherapy response is represented by different colors
  • the survival time is represented by the size of the point. Survival status is represented by different shapes, and a scatterplot is drawn. Further observe whether the samples with good prognosis have a central tendency, so as to judge the relationship between the indicators and the efficacy of immunotherapy.
  • the analysis data set is the gene expression files of each cancer type.
  • the files come from the transcriptome sequencing technology (RNA-seq) data of The Cancer Genome Atlas (TCGA).
  • RNA-seq transcriptome sequencing technology
  • TCGA Cancer Genome Atlas
  • the clinical follow-up data comes from the supplementary material of An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics literature.
  • the reference data set by cancer type is empty, and a supplementary data set is added, which is a self-built RNAseq sample data set of 201 cases, which is used for the analysis of immune cell component estimation.
  • Table 1 The TCGA dataset information is shown in Table 1.
  • xCell software is used to calculate the score of the cellular components in the sample.
  • the types of immune cells that can be detected by xCell software are listed in Table 2:
  • xCell software is a software based on eigengene algorithm. Its core algorithm is single sample Gene Set Enrichment Analysis (ssGSEA). This algorithm can effectively avoid the dependence of feature matrix, and its results are more reliable.
  • ssGSEA single sample Gene Set Enrichment Analysis
  • the xCell software also has its limitations. It requires that the sample size input at the same time be as large as possible during the analysis, so that the analysis results are more accurate.
  • a supplementary data set is introduced, which contains samples of many cancer types, controls, and standard samples.
  • Each sample in the analysis data set was merged into a supplementary data set for xCell analysis, and the immune cell infiltration score of each sample in each TCGA data set was extracted after analysis, which was merged according to the data set as the tumor infiltrating immune cell component of the data set result.
  • a separate xCell analysis was performed on the Supplementary Dataset as a result of the tumor-infiltrating immune cell fraction of the Supplementary Dataset.
  • the DC cell infiltration score in the ACC data set and the supplementary data set were selected as the distribution estimation set of DC cells in the ACC data set.
  • the distribution interval is determined according to the minimum and maximum values in the estimation set, the interval is divided into ten equal parts, the frequency of each sub-interval is counted and the frequency of each sub-interval is calculated, which is used as the probability distribution estimation of DC cells in the ACC dataset.
  • the contribution of immune components is further calculated.
  • the ACC data set is still used as an example here.
  • the contribution of its DC cells is -log 2 p DC_1 , where p DC_1 represents the probability corresponding to the distribution subinterval where the DC cell infiltration score of the s1 sample is located.
  • p DC_1 represents the probability corresponding to the distribution subinterval where the DC cell infiltration score of the s1 sample is located.
  • the contributions of all immune cell types in the s1 sample are calculated and summed to obtain the immune component contribution score of the sample.
  • the formula is as follows:
  • An ICS value can be calculated for each sample in the data set, and the ICS index can be further investigated.
  • ICS score is a continuous variable without a clear threshold division.
  • the surv_cutpoint function is used to determine the optimal grouping threshold for survival analysis, and the ICS-high group and ICS-low group are divided by this threshold, and then the survival analysis is performed.
  • Overall survival (OS, Overall Survival) and progression-free survival (PFI, Progression Free Interval) were selected as survival data in the clinical follow-up data.
  • the p value of survival analysis was less than 0.05, which indicated that ICS was significantly related to prognosis.
  • Figures 2A-2V show the graphs of the survival analysis results of the TCGA data set with significantly correlated PFI.
  • Table 3 shows the results of ICS survival analysis on the TCGA dataset.
  • the analysis data set in this example comes from the gene expression database GEO created and maintained by the National Center for Biotechnology Information NCBI in the United States, and the data set number is GSE78220.
  • This data set is a data set of mRNA expression of melanoma before anti-PD-1 checkpoint inhibition therapy, including 28 samples.
  • the clinical information of the samples including survival time, survival status, and response to immune checkpoint inhibitor therapy, were downloaded from their corresponding literature. Since different cancer types have their own immune microenvironmental characteristics, it would be more meaningful if a reference data set corresponding to the cancer type can be selected according to the cancer type of the sample to be analyzed.
  • the TCGA data set corresponding to the cancer type is added as a reference data set for each cancer type, and the SKCM data set is used in this embodiment. Since the sample size of certain cancer types in the TCGA data set is relatively small, in order to increase stability and universality, the supplementary data set in Example 1 was also introduced for joint analysis.
  • the xCell software is used to calculate the score of the cell components in the sample.
  • the specific process refer to step 2 of Example 1.
  • the GSE78220 dataset, TCGA's SKCM dataset and supplementary datasets are selected as distribution estimation sets. For the specific process, refer to step 3 of Example 1.
  • the contribution of immune components is further calculated.
  • step 4 of Example 1 the probability distribution of tumor infiltrating immune cell components obtained in the previous step.
  • ICS score is a continuous variable without a clear threshold division.
  • the inventor first used the surv_cutpoint function to determine the optimal grouping threshold for survival analysis, and then divided the ICS-high group and ICS-low group based on this threshold, and then performed survival analysis.
  • the clinical follow-up data of this embodiment is the overall survival period.
  • Fig. 3 shows the result of survival analysis in this embodiment. It can be seen that the prognosis of the ICS-high group is significantly better than that of the ICS-low group, and ICS is significantly related to the survival of patients.

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Abstract

一种评估肿瘤免疫微环境的评分模型及其构建方法，涉及生物信息领域。利用该方法构建获得的评分模型，全面衡量了肿瘤微环境免疫组分的程度，该模型着眼于各种免疫细胞组分与平均水平的差异，将难以叠加的差异水平转化为概率模型，解决了浸润水平量纲不统一的难题。

Description

评估肿瘤免疫微环境的评分模型及其构建方法 技术领域
本公开涉及生物信息领域，具体地，本公开涉及一种评估肿瘤免疫微环境的评分模型及其构建方法。
背景技术
肿瘤微环境(Tumor microvironment，TME)是一个高度复杂、动态的系统，主要由肿瘤细胞及其周围的免疫细胞、肿瘤相关的成纤维细胞、附近的间质组织、微血管等等构成。在这个环境中，肿瘤细胞与其邻近的细胞处于频繁的交流之中，这种交流是动态的、双向的，在交流中肿瘤不断改变维持自身生存和发展的条件，进而影响其生长、侵袭和转移。肿瘤免疫编辑学说作为当前被认可的肿瘤免疫逃逸理论，将肿瘤的发展分为三个阶段，其中第一个阶段——清除期，机体的免疫监视功能通过抗肿瘤免疫效应机制发挥抗肿瘤作用。免疫系统之所以在免疫监视中发挥重要的作用，是因为适应性免疫系统的免疫细胞(如T细胞、B细胞)和先天性免疫系统的免疫细胞(如NK细胞、DC细胞等)浸润到肿瘤微环境中，调控肿瘤进展。而肿瘤发展的第三个阶段—免疫逃逸期，肿瘤通过第二阶段平衡期的免疫编辑具备了抵抗免疫系统清除的功能，并发展为具有临床表现的肿瘤。
肿瘤微环境中的免疫浸润细胞发挥的作用不容小觑，研究方法也愈发丰富。从前对于浸润的免疫细胞研究多是基于免疫组化或免疫荧光技术，现在随着NGS技术的不断发展，RNAseq应用于肿瘤浸润细胞组分的评估方法也逐渐成熟，各种软件如xCell:Aran D,Hu Z,Butte A J.xCell(Digitally portraying the tissue cellular heterogeneity landscape.2017.)、CIBERSORT(Chen B,Khodadoust M S,Liu C L,et al.Profiling Tumor Infiltrating Immune Cells with CIBERSORT[J].Methods in Molecular Biology,2018,1711:243.)、Timer(Li T,Fan J,Wang B,et al.TIMER:A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells[J].Cancer Research,2017,77(21):e108.)等都广泛应用于科学研究中，它们使用反卷积算法或标记基因算法衡量出肿瘤中各类细胞组分的浸润水平。目前流行的Estimate(Yoshihara K,Shahmoradgoli M,E Martínez,et al.Inferring tumour purity and stromal and immune cell admixture from expression data[J].Nature Communications,2013,4.)算法也在一定程度评估了肿瘤微环境中的免疫组分情况。
虽然上述软件都从不同角度反映了免疫微环境，但鲜有全面反映免疫浸润水平的指标。因此，亟需开发一种能够全面反映肿瘤微环境免疫浸润水平指标的方法。
发明内容
本公开旨在至少在一定程度上解决相关技术中的技术问题之一。为此，本公开提供了一种用于评估肿瘤免疫微环境的评分模型的构建方法，利用该方法构建获得的评分模型，全面衡量了肿瘤微环境免疫组分的程度，该模型着眼于各种免疫细胞组分与平均水平的差异，将难以叠加的差异水平转化为概率模型，解决了浸润水平量纲不统一的难题。
在本公开的第一方面，本公开提出了一种用于评估肿瘤免疫微环境的评分模型的构建方法。根据本公开的实施方案，所述方法包括：
(1)收集数据，所述数据包括分析数据集，其中，所述分析数据集包括多个癌种的转录组测序数据，每个癌种包括多个样本；
(2)计算所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分；
(3)利用步骤(2)获得的所述每一种免疫细胞类型组分的浸润得分确定相应免疫细胞类型组分得分的分布区间，获得肿瘤浸润免疫细胞组分概率分布；
(4)利用所述肿瘤浸润免疫细胞组分概率分布，针对所述分析数据集中每个样本的每一种细胞类型，计算对免疫组分的贡献度，构建用于评估肿瘤免疫微环境的评分模型，
所述评分模型为：
ICS＝-Σlog 2p cell_i
其中，所述p cell_i代表所述样本中属于免疫组分的一类细胞组分得分出现的概率，i表示细胞种类，所述ICS为所述样本中属于免疫细胞组分的一类细胞对免疫组分的贡献度。
现有的RNAseq应用于肿瘤浸润细胞组分的评估方法，不能全面反映免疫浸润水平的指标。目前流行的Estimate算法，开发的初衷是估计肿瘤纯度，也并非为了反映肿瘤免疫微环境的情况。基于此，发明人获得上述第一方面所述的用于评估肿瘤免疫微环境的评分模型的构建方法，利用该方法构建获得的评分模型，不同于以往的肿瘤免疫微环境评估模式，本方法以贡献度的方式量化出各类细胞偏离一般水平的程度，反映出了微环境中多种类免疫细胞的总体水平与普遍水平的差别，能够全面评估免疫微环境情况，从而实现免疫微环境差异对于预后带来的影响的研究。
根据本公开的实施方案，所述数据进一步包括补充数据集，所述补充数据集包括多个癌种样本以及标准品样本的转录组测序数据。
根据本公开的实施方案，所述标准品样本选自人类通用参考RNA标准品样本。
根据本公开的实施方案，步骤(2)进一步包括：
1)将所述分析数据集中的每个样本的数据分别与所述补充数据集合并，获得合并后数 据集；
2)计算所述合并后数据集中每个样本中每一种免疫细胞类型组分的浸润得分，提取所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分。
根据本公开的实施方案，步骤(3)进一步包括：
ⅰ)将所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分和所述补充数据集中每个样本中每一种免疫细胞类型组分的浸润得分作为所述分析数据集中每个样本中每一种免疫细胞类型的分布估计集，根据所述分布估计集中的最大值和最小值，确定分布区间；
ⅱ)将所述分布区间等分，获得多个子区间，统计每个所述子区间的频数，获得每个所述子区间的频率，基于每个所述子区间的频数和每个所述子区间的频率获得肿瘤浸润免疫细胞组分概率分布。
根据本公开的实施方案，将所述分布区间等分为8～12个子区间。
根据本公开的实施方案，所述每一种免疫细胞类型组分的浸润得分通过肿瘤免疫浸润组分分析软件获取。
根据本公开的实施方案，所述肿瘤免疫浸润组分分析软件选自Timer软件、CIBERSORT软件或xCell软件。
根据本公开的实施方案，所述肿瘤免疫浸润组分分析软件为xCell软件。
基于现有衡量指标的局限性，本公开提供的用于评估肿瘤免疫微环境的评分模型的构建方法，整合基于xCell等算法计算的肿瘤中各类细胞组分的水平，全面衡量肿瘤免疫微环境。相比于其他同类软件，xCell考察的细胞类型更多，高达64种，其中免疫细胞高达34种，这是其他软件无法比拟的。
本公开第二方面提供一种评估肿瘤免疫微环境的评分模型。根据本公开的实施方案，所述评分模型通过第一方面所述的构建方法构建获得。
本公开第三方面提供第二方面所述的评估肿瘤免疫微环境的评分模型在评估肿瘤免疫微环境中的用途。
本公开第四方面提供一种评估肿瘤免疫微环境的方法。根据本公开的实施方案，所述方法包括：
利用第二方面所述的评估肿瘤免疫微环境的评分模型，获取待测样本中预定癌种的属于免疫组分的细胞的ICS值，将所述ICS值与预设ICS值进行比较，评估肿瘤免疫微环境。
本公开第五方面提供第二方面所述的评估肿瘤免疫微环境的评分模型在肿瘤患者的预后评估中的用途。
本公开第六方面提供一种肿瘤患者的预后评估方法。根据本公开的实施方案，所述方 法包括：
Ⅰ)利用第二方面所述的评估肿瘤免疫微环境的评分模型，获取分析数据集中预定癌种的属于免疫组分的细胞的ICS值，建立ICS值与肿瘤患者生存的关系，确定ICS值对于患者生存的最优分组阈值；
Ⅱ)利用所述评估肿瘤免疫微环境的评分模型获取待测肿瘤患者的样本的ICS值，通过Ⅰ)中所述的ICS值对于患者生存的最优分组阈值，获取所述肿瘤患者的预后情况。
发明人发现，在多个癌种的样本集中，ICS指标与患者生存显著相关，即使这些患者并没有进行免疫治疗。因此，根据ICS指标与肿瘤患者生存分析的关系，能够获取肿瘤患者的预后情况。
根据本公开的实施方案，步骤Ⅰ)进一步包括：
根据所述分析数据集中每个样本对应的临床随访数据，获取总生存期或无进展生存期，将所述总生存期或无进展生存期作为生存数据，获取ICS值与肿瘤患者生存分析的关系。
根据本公开的实施方案，所述临床随访数据包括患者的生存时间、生存状态以及对免疫检查点抑制剂治疗的响应情况。
本公开第七方面提供第二方面所述的评估肿瘤免疫微环境的评分模型在评估免疫治疗疗效中的用途。
本公开第八方面提供一种免疫治疗疗效的评估方法。根据本公开的实施方案，所述方法包括：
a)利用第二方面所述的评估肿瘤免疫微环境的评分模型，获取分析数据集中预定癌种的属于免疫组分的细胞的ICS值，根据患者对免疫检查点抑制剂治疗的响应情况，建立ICS值联合基因标志物的表达水平的分组关系，其中，所述基因标志物为与肿瘤发生、发展相关的基因标志物；
b)利用所述评估肿瘤免疫微环境的评分模型获取源自免疫检查点抑制剂治疗的响应情况未知的待测肿瘤患者的样本在免疫治疗前的ICS值及基因标志物的表达水平，通过a)中所述的联合分组关系，评估免疫治疗疗效。
发明人发现，根据患者的免疫治疗数据集，发现ICS值联合肿瘤基因标志物表达的分组关系与免疫治疗疗效相关。因此，根据ICS指标与肿瘤患者在免疫治疗前的基因标志物的表达水平，能够评估免疫治疗疗效。
本公开的附加方面和优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。
附图说明
本公开的上述和/或附加的方面和优点从结合下面附图对实施例的描述中将变得明显和容易理解，其中：
图1显示了本公开一个实施例的构建评估肿瘤免疫微环境的评分模型的方法的流程图；
图2A-2V显示了实施例1中PFI显著相关的TCGA数据集生存分析结果图；
图3显示了实施例2中的ICS与预后的关系；
图4显示了实施例2中的ICS与免疫治疗疗效的关系，其中，免疫治疗响应程度用不同颜色表示，黑色代表完全响应，深灰色代表部分响应，浅灰色代表疾病发生进展。点的大小表示了样本生存时间，点越大患者生存时间越长。生存状态则用不同的形状表示，圆形代表患者仍处于生存状态，三角形代表患者已经死亡。图中增加了两条虚线作为辅助将样本分为了四个部分。
发明详细描述
下面详细描述本公开的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本公开，而不能理解为对本公开的限制。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。在本公开的描述中，“多个”的含义是至少两个，例如两个，三个等，除非另有明确具体的限定。
针对缺少全面衡量肿瘤微环境免疫浸润水平指标的问题，发明人开发了ICS(Immune Contribution Signature)评分模型。
在本公开的一个具体的实施方案中，提供一种用于评估肿瘤免疫微环境的评分模型的构建方法，如图1所示，该方法包括步骤：
S1、数据准备：
数据包括分析数据集、分癌种参考数据集，可选的，增加补充数据集。
S2、肿瘤浸润免疫细胞组分分析：
使用肿瘤免疫浸润组分分析软件计算样本中每一种免疫细胞组分的得分。
S3、肿瘤浸润免疫细胞组分分布估计：
根据得到的各个免疫细胞类型组分得分量化免疫组分的水平。发现不同类型的细胞组分其得分分布范围各不相同，有些细胞组分得分整体都比较高，有些却比较低，有些细胞组分得分变化范围比较大，有些却比较小。量纲不同成为了肿瘤浸润免疫组分全面估计的关键性问题。如果只是单纯的将所有免疫细胞组分得分简单的进行加和，那本身浸润水平 高的细胞类型占的比重会很大，而本身浸润水平低的细胞类型其比重就会很小，甚至可以忽略不计。为了消除这种量纲不同造成的偏倚，将对一种免疫细胞浸润水平的评估转换为该细胞对免疫组分的贡献度的评估。
发明人认为，如果一个样本中某种免疫细胞的浸润水平和大量其他样本相比显著不同，如明显的高于其他样本，那么这个样本中该类细胞对免疫组分的贡献度就要大于其他样本中该细胞的贡献度。因此，可以根据大量样本中的各细胞类型的浸润水平统计出浸润水平分布，之后考察每个待考察样本的每类细胞在该分布中出现的概率，如果出现的概率低，就认为贡献度高，如果出现的概率高，就认为其贡献度低。
对于全部样本的每一种细胞类型，统计其最小值和最大值，根据此最小值和最大值确定该类细胞组分得分的分布区间，将该区间十等分，统计每个子区间的频数并计算每个子区间的频率，以此作为肿瘤浸润免疫细胞组分的概率分布估计。
S4、免疫组分贡献度计算：
根据上一步得到的肿瘤浸润免疫细胞组分的概率分布，对于分析数据集中的某一样本，考察其每一种细胞类型组分得分分别落入哪个子区间，每个组分对应的概率记作p cell_i。每一个样本均以下述公式计算样本的免疫组分贡献度：
ICS＝-Σlog 2p cell_i
其中p cell_i代表该样本中属于免疫组分的某类细胞组分得分出现的概率，i表示细胞种类，ICS为该样本中属于免疫组分的某类细胞组分对免疫组分的贡献度。
S5、与预后的关系
使用R软件包survival、survminer对计算了ICS的样本进行Kaplan Meier生存分析。ICS分数是没有明确阈值划分的连续变量，这里先使用surv_cutpoint函数确定出生存分析的最佳分组阈值，以此阈值划分出ICS-high组和ICS-low组，进而进行生存分析。生存分析p值小于0.05，代表ICS与预后显著相关。
S6、与免疫治疗疗效的关系
对于有免疫治疗疗效信息的样本，提取分析数据集中每个样本的CD274基因表达值作为x轴坐标，ICS值作为y轴坐标，免疫治疗反应程度用不同颜色表示，生存时间用点的大小表示，生存状态用不同的形状表示，绘制散点图。进一步观察预后好的样本是否有集中趋势，以此判断指标与免疫治疗疗效间的关系。
下面将结合实施例对本公开的方案进行解释。本领域技术人员将会理解，下面的实施例仅用于说明本公开，而不应视为限定本公开的范围。实施例中未注明具体技术或条件的，按照本领域内的文献所描述的技术或条件或者按照产品说明书进行。所用试剂或仪器未注 明生产厂商者，均为可以通过市购获得的常规产品。
实施例1
1、数据准备
为全面考察各癌种的ICS指标情况，分析数据集为各个癌种的基因表达文件，文件来自于癌症基因图谱(TCGA)的转录组测序技术(RNA-seq)数据，除此之外，还要下载其对应的临床随访数据，临床随访数据来源于An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics文献的补充材料。此实例中分癌种参考数据集为空，另外增加了补充数据集，为自主建立的201例RNAseq样本数据集，该补充数据集用于免疫细胞组分估计的分析。TCGA数据集信息示于表1。
表1
Figure PCTCN2021142257-appb-000001
Figure PCTCN2021142257-appb-000002
2、肿瘤浸润免疫细胞组分分析
此实例中使用xCell软件计算样本中细胞组分的得分。xCell软件可检测的免疫细胞类型如下表2：
表2
Figure PCTCN2021142257-appb-000003
Figure PCTCN2021142257-appb-000004
xCell软件是一款基于特征基因算法的软件，其核心算法为单样本基因集富集分析(single sample Gene Set Enrichment Analysis，ssGSEA)，该算法可以有效避免特征矩阵的依赖，其结果更可靠。但是，xCell软件也有其局限性，它要求分析时同时输入的样本量尽量大，这样分析结果更准确。为了规避xCell软件的局限性，增加分析稳定性，引入了补充数据集，该数据集中包含了众多癌种样本以及对照、标准品样本等。将分析数据集中的每一例样本分别合并补充数据集进行xCell分析，分析后提取每个TCGA数据集中每个样本的免疫细胞浸润得分，按数据集合并后作为该数据集的肿瘤浸润免疫细胞组分结果。补充数据集单独进行一次xCell分析，分析结果作为补充数据集的肿瘤浸润免疫细胞组分结果。
3、肿瘤浸润免疫细胞组分分布估计
对于每一个数据集的每一种细胞类型，分别估计其浸润得分的分布情况。下面以ACC数据集DC细胞为例进行详细说明。
选取ACC数据集和补充数据集中DC细胞浸润得分作为ACC数据集DC细胞的分布估计集。根据估计集中的最小值和最大值确定分布区间，将该区间十等分，统计每个子区间的频数并计算每个子区间的频率，以此作为ACC数据集DC细胞的概率分布估计。
4、免疫组分贡献度计算
根据上一步得到的肿瘤浸润免疫细胞组分的概率分布，进一步进行免疫组分贡献度的计算。
这里仍以ACC数据集举例，对于某一样本s1，其DC细胞的贡献度为﹣log 2p DC_1，其中，p DC_1代表s1样本的DC细胞浸润得分所在的分布子区间对应的概率。类似地，将s1样本全部免疫细胞类型的贡献度分别计算后进行加和，即得到该样本的免疫组分贡献度得分，公式如下：
ICS＝-Σlog 2p cell_i
数据集中每个样本都可以计算出一个ICS值，进一步考察ICS指标。
5、与预后的关系
使用R软件包survival、survminer对每个数据集分别进行Kaplan Meier生存分析。ICS分数是没有明确阈值划分的连续变量，这里先使用surv_cutpoint函数确定出生存分析的最佳分组阈值，以此阈值划分出ICS-high组和ICS-low组，进而进行生存分析。临床随访数 据中选择总生存期(OS，Overall Survival)和无进展生存期(PFI，Progression Free Interval)作为生存数据。生存分析p值小于0.05，代表ICS与预后显著相关。图2A-2V展示了PFI显著相关的TCGA数据集生存分析结果图。表3显示了TCGA数据集的ICS生存分析结果。
表3
数据集名称 OS_p值 PFI_p值
ACC 0.00033 0.00045
BLCA 0.3 0.028
BRCA 0.2 0.053
CESC 0.081 0.024
CHOL 0.055 0.018
COAD 0.015 0.03
DLBC 0.02 0.0016
ESCA 0.001 0.01
GBM 0.1 0.00027
HNSC 0.14 0.13
KICH 0.07 0.03
KIRC 0.0078 0.0022
KIRP 0.03 0.012
LAML 0.04 -
LGG 0.31 0.17
LIHC 0.023 0.0039
LUAD 0.1 0.08
LUSC 0.03 0.019
MESO 0.39 0.0044
OV 0.09 0.08
PAAD 0.14 0.08
PCPG 0.0029 0.0043
PRAD 0.019 0.015
READ 0.07 0.21
SARC 0.09 0.21
SKCM 0.0013 0.025
STAD 0.11 0.17
TGCT 0.11 0.16
THCA 0.06 0.04
THYM 0.029 0.0037
UCEC 0.025 0.035
UCS 0.19 0.04
UVM 0.0048 0.046
上述分析结果可以得出，在大部分的癌种中，ICS结果均与患者生存显著相关，特别是PFI的生存分析结果，说明ICS反映的免疫微环境情况与癌症进展相关性很强。
实施例2
1、数据准备
为考察任一分析数据集的ICS指标情况，本实施例中分析数据集来自于美国国立生物技术信息中心NCBI创建并维护的基因表达数据库GEO，数据集编号为GSE78220。该数据集为抗pd-1检查点抑制治疗前黑色素瘤的mRNA表达的数据集，包含28例样本。另外，从其对应的文献中下载样本的临床信息，包括生存时间、生存状态以及对免疫检查点抑制剂治疗的响应情况。由于不同的癌症种类有其本身的免疫微环境特点，如果可以根据待分析样本的癌症种类选择对应癌种的参考数据集会更有意义。因此，对于TCGA中包含癌种的数据集，增加对应癌种的TCGA数据集作为分癌种参考数据集，此实施例中使用了SKCM数据集。由于TCGA数据集中某些癌种的样本量也比较小，为了增加稳定性和普适性，也引入实施例1中的补充数据集共同分析。
特别地，对于此GEO分析数据集，需先进行探针名-基因名转换的准备工作。
2、肿瘤浸润免疫细胞组分分析
此实例中使用xCell软件计算样本中细胞组分的得分，具体过程参考实施例1的步骤2。
3、肿瘤浸润免疫细胞组分分布估计
对于GSE78220数据集的每一种细胞类型，分别估计其浸润得分的分布情况。选取GSE78220数据集、TCGA的SKCM数据集和补充数据集作为分布估计集。具体过程参考实施例1的步骤3。
4、免疫组分贡献度计算
根据上一步得到的肿瘤浸润免疫细胞组分的概率分布，进一步进行免疫组分贡献度的计算。具体过程参考实施例1的步骤4。
5、与预后的关系
使用R软件包survival、survminer对每个数据集分别进行Kaplan Meier生存分析。ICS分数是没有明确阈值划分的连续变量，这里发明人先使用surv_cutpoint函数确定出生存分析的最佳分组阈值，以此阈值划分出ICS-high组和ICS-low组，进而进行生存分析。此实施例的临床随访数据为总生存期。图3展示了此实施例的生存分析结果图，可以看出ICS-high组的预后明显优于ICS-low组，ICS与患者生存情况显著相关。
6、与免疫治疗疗效的关系
提取GSE78220数据集中每个样本的CD274基因(即PD-L1)表达值作为x轴坐标，ICS值作为y轴坐标绘制散点图，见图4。免疫治疗响应程度用不同颜色表示，黑色代表完全响应，深灰色代表部分响应，浅灰色代表疾病发生进展。点的大小表示了样本生存时间， 点越大患者生存时间越长。生存状态则用不同的形状表示，圆形代表患者仍处于生存状态，三角形代表患者已经死亡。图中增加了两条虚线作为辅助将样本分为了四个部分，不难看出对于免疫治疗的响应效果好的样本大多位于ICS-high且PD-L1高表达的区域，ICS联合PD-L1表达的指标或对免疫治疗疗效起到预测作用。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
尽管上面已经示出和描述了本公开的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本公开的限制，本领域的普通技术人员在本公开的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (16)

  1. 一种用于评估肿瘤免疫微环境的评分模型的构建方法，其中，所述方法包括：
    (1)收集数据，所述数据包括分析数据集，其中，所述分析数据集包括多个癌种的转录组测序数据，每个癌种包括多个样本；
    (2)计算所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分；
    (3)利用步骤(2)获得的所述每一种免疫细胞类型组分的浸润得分确定相应免疫细胞类型组分得分的分布区间，获得肿瘤浸润免疫细胞组分概率分布；
    (4)利用所述肿瘤浸润免疫细胞组分概率分布，针对所述分析数据集中每个样本的每一种细胞类型，计算对免疫组分的贡献度，构建用于评估肿瘤免疫微环境的评分模型，
    所述评分模型为：
    ICS＝-Σlog 2p cell_i
    其中，所述p cell_i代表所述样本中属于免疫组分的一类细胞组分得分出现的概率，i表示细胞种类，所述ICS为所述样本中属于免疫细胞组分的一类细胞对免疫组分的贡献度。
  2. 根据权利要求1所述的构建方法，其中，所述数据进一步包括补充数据集，所述补充数据集包括多个癌种样本以及标准品样本的转录组测序数据；
    任选地，所述标准品样本选自人类通用参考RNA标准品样本。
  3. 根据权利要求2所述的构建方法，其中，步骤(2)进一步包括：
    1)将所述分析数据集中的每个样本的数据分别与所述补充数据集合并，获得合并后数据集；
    2)计算所述合并后数据集中每个样本中每一种免疫细胞类型组分的浸润得分，提取所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分。
  4. 根据权利要求2或3所述的构建方法，其中，步骤(3)进一步包括：
    ⅰ)将所述分析数据集中每个样本中每一种免疫细胞类型组分的浸润得分和所述补充数据集中每个样本中每一种免疫细胞类型组分的浸润得分作为所述分析数据集中每个样本中每一种免疫细胞类型的分布估计集，根据所述分布估计集中的最大值和最小值，确定分布区间；
    ⅱ)将所述分布区间等分，获得多个子区间，统计每个所述子区间的频数，获得每个所述子区间的频率，基于每个所述子区间的频数和每个所述子区间的频率获得肿瘤浸润免疫细胞组分概率分布。
  5. 根据权利要求4所述的构建方法，其中，将所述分布区间等分为8～12个子区间。
  6. 根据权利要求1-5中任一项所述的构建方法，其中，所述每一种免疫细胞类型组分 的浸润得分通过肿瘤免疫浸润组分分析软件获取。
  7. 根据权利要求6所述的构建方法，其中，所述肿瘤免疫浸润组分分析软件选自Timer软件、CIBERSORT软件或xCell软件。
  8. 一种评估肿瘤免疫微环境的评分模型，其中，所述评分模型通过权利要求1-7中任一项所述的构建方法构建获得。
  9. 权利要求8所述的评估肿瘤免疫微环境的评分模型在评估肿瘤免疫微环境中的用途。
  10. 一种评估肿瘤免疫微环境的方法，其中，所述方法包括：
    利用权利要求8所述的评估肿瘤免疫微环境的评分模型，获取待测样本中预定癌种的属于免疫组分的细胞的ICS值，将所述ICS值与预设ICS值进行比较，评估肿瘤免疫微环境。
  11. 权利要求8所述的评估肿瘤免疫微环境的评分模型在肿瘤患者的预后评估中的用途。
  12. 一种肿瘤患者的预后评估方法，其中，所述方法包括：
    Ⅰ)利用权利要求8所述的评估肿瘤免疫微环境的评分模型，获取分析数据集中预定癌种的属于免疫组分的细胞的ICS值，建立ICS值与肿瘤患者生存的关系，确定ICS值对于患者生存的最优分组阈值；
    Ⅱ)利用所述评估肿瘤免疫微环境的评分模型获取待测肿瘤患者的样本的ICS值，通过Ⅰ)中所述的ICS值对于患者生存的最优分组阈值，获取所述肿瘤患者的预后情况。
  13. 根据权利要求12所述的预后评估方法，其中，步骤Ⅰ)进一步包括：
    根据所述分析数据集中每个样本对应的临床随访数据，获取总生存期或无进展生存期，将所述总生存期或无进展生存期作为生存数据，获取ICS值与肿瘤患者生存分析的关系。
  14. 根据权利要求13所述的预后评估方法，其中，所述临床随访数据包括患者的生存时间、生存状态以及对免疫检查点抑制剂治疗的响应情况。
  15. 权利要求8所述的评估肿瘤免疫微环境的评分模型在评估免疫治疗疗效中的用途。
  16. 一种免疫治疗疗效的评估方法，其中，所述方法包括：
    a)利用权利要求8所述的评估肿瘤免疫微环境的评分模型，获取分析数据集中预定癌种的属于免疫组分的细胞的ICS值，根据患者对免疫检查点抑制剂治疗的响应情况，建立ICS值联合基因标志物的表达水平的分组关系，其中，所述基因标志物为与肿瘤发生、发展相关的基因标志物；
    b)利用所述评估肿瘤免疫微环境的评分模型获取源自免疫检查点抑制剂治疗的响应情况未知的待测肿瘤患者的样本在免疫治疗前的ICS值及基因标志物的表达水平，通过a)中所述的联合分组关系，评估免疫治疗疗效。
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